SNNS (Stuttgart Neural Network Simulator)

Zell, Andreas; Mache, Niels; Hübner, Ralf; Mamier, Günter; Vogt, Michael; Schmalzl, Michael; Herrmann, Kai-Uwe

doi:10.1007/978-1-4615-2736-7_9

Cited by 195 publications

(173 citation statements)

References 7 publications

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“…O software utilizado neste trabalho foi o JavaSNNS (ZELL et al, 1998) No pós-processamento foi utilizada uma transformação inversa ao pré-processamento, acompanhada de uma regra lógica: "se X < 0, então X = 0; caso contrário X = X". Tal regra foi importante para assegurar que não ocorressem valores inferiores a zero (realismo biológico).…”

Section: Methodsunclassified

Estimação do volume de árvores utilizando redes neurais artificiais

Görgens

Leite²,

Santos

et al. 2009

Rev. Árvore

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RESUMO -Rede neural artificial consiste em um conjunto de unidades que contêm funções matemáticas, unidas por pesos. As redes são capazes de aprender, mediante modificação dos pesos sinápticos, e generalizar o aprendizado para outros arquivos desconhecidos. O projeto de redes neurais é composto por três etapas: pré-processamento, processamento e, por fim, pós-processamento dos dados. Um dos problemas clássicos que podem ser abordados por redes é a aproximação de funções. Nesse grupo, pode-se incluir a estimação do volume de árvores. Foram utilizados quatro arquiteturas diferentes, cinco pré-processamentos e duas funções de ativação. As redes que se apresentaram estatisticamente iguais aos dados observados também foram analisadas quanto ao resíduo e à distribuição dos volumes e comparadas com a estimação de volume pelo modelo de Schumacher e Hall. As redes neurais formadas por neurônios, cuja função de ativação era exponencial, apresentaram estimativas estatisticamente iguais aos dados observados. As redes treinadas com os dados normalizados pelo método da interpolação linear e equalizados tiveram melhor desempenho na estimação.Palavras-chave: Inteligência artificial, cubagem e árvores individuais. ESTIMATE OF TREE VOLUME USING ARTIFICIAL NEURAL NETS ABSTRACT -The artificial neural network consists of a set of units containing mathematical functions connected by weights. Such nets are capable of learning by means of synaptic weight modification, generalizing learning for other unknown archives. The neural network project comprises three stages

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Section: Methodsunclassified

Estimação do volume de árvores utilizando redes neurais artificiais

Görgens

Leite²,

Santos

et al. 2009

Rev. Árvore

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“…Therefore, it is not necessary to take a lot of effort to select the optimal values, and it is more efficient to select appropriate small learning parameters considering the computation speed (convergence speed). In this study, the default values of learning parameters suggested by Zell et al [93] were used.…”

Section: Models Ds and As Input Variables Output Variablesmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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“…7,000 samples of background clutter and 3,000 samples each of both parts of the landing pad (circle and letter "H"), taken from different perspectives. After training the network, we can generate efficient C code using the snns2c module of Stuttgart Neural Network Simulator (SNNS) [25].…”

Section: ) Classification Of Connected Componentsmentioning

confidence: 99%

An Onboard Monocular Vision System for Autonomous Takeoff, Hovering and Landing of a Micro Aerial Vehicle

Yang

Scherer

Zell

2012

J Intell Robot Syst

Self Cite

150

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Abstract-In this paper, we present an onboard monocular vision system for autonomous takeoff, hovering and landing of a Micro Aerial Vehicle (MAV). Since pose information with metric scale is critical for autonomous flight of a MAV, we present a novel solution to six degrees of freedom (DOF) pose estimation. It is based on a single image of a typical landing pad which consists of the letter "H" surrounded by a circle. A vision algorithm for robust and real-time landing pad recognition is implemented. Then the 5 DOF pose is estimated from the elliptic projection of the circle by using projective geometry. The remaining geometric ambiguity is resolved by incorporating the gravity vector estimated by the inertial measurement unit (IMU). The last degree of freedom pose, yaw angle of the MAV, is estimated from the ellipse fitted from the letter "H". The efficiency of the presented vision system is demonstrated comprehensively by comparing it to ground truth data provided by a tracking system and by using its pose estimates as control inputs to autonomous flights of a quadrotor.

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SNNS (Stuttgart Neural Network Simulator)

Cited by 195 publications

References 7 publications

Estimação do volume de árvores utilizando redes neurais artificiais

Estimação do volume de árvores utilizando redes neurais artificiais

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

An Onboard Monocular Vision System for Autonomous Takeoff, Hovering and Landing of a Micro Aerial Vehicle

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